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Robust Inference of Gene Regulatory Networks
KTH, School of Electrical Engineering (EES), Automatic Control.
KTH, School of Electrical Engineering (EES), Automatic Control.
2012 (English)Conference paper, Poster (Refereed)
Abstract [en]

To successfully infer the biochemical network that underly a given biological function, two problems must be resolved. First, sufficiently informative data that allow discrimination between alternative models corresponding to different network structures must be recorded. Second, the ''correct'' network model with a structure including only the active interactions must be selected based on the recorded data set. In this work we address both these problems within the framework of robust inference. We first address the problem in a deterministic framework and show that determination of the existence of a specific interaction, or directed network edge, can be reduced to a rank test on a matrix constructed from available perturbation and response data. To deal with uncertainty, we introduce a norm-bounded set around the nominal data points in the sample space and assume the true response of the system is within this set. A similar uncertainty description is employed to describe uncertainties in the applied perturbations. Determination of the existence of a specific interaction under uncertainty can then be formulated as a robust rank problem, which can be solved using results from robust control theory. The proposed method provides necessary and sufficient conditions for the existence of a directed network edge under the assumption that the true response is within the given uncertainty set. Similarly, network edges can be determined with a robustness margin, i.e., the size of the uncertainty set for which the edge can identified with confidentiality. An important outcome of the method is determination of interactions for which the available data set does not contain sufficient information to infer existence or non-existence with confidence. Furthermore, based on well known results from linear algebra, we show how specific perturbation experiments can be designed to generate data that enable inference of a specific edge at a given level of confidence.

Place, publisher, year, edition, pages
National Category
Control Engineering Bioinformatics and Systems Biology
URN: urn:nbn:se:kth:diva-104597OAI: diva2:565234
The 13th International Conference on Systems Biology, Toronto, Canada, August 19-23, 2012
ICT - The Next Generation

QC 20130109

Available from: 2012-11-06 Created: 2012-11-06 Last updated: 2013-04-15Bibliographically approved

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Jacobsen, Elling W.Nordling, Torbjörn
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